Instructions to use hf-internal-testing/tiny-random-T5ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-T5ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-T5ForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-T5ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-T5ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-T5ForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 222 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-T5ForTokenClassification/resolve/refs%2Fpr%2F7/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-T5ForTokenClassification@refs/pr/7/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-T5ForTokenClassification/resolve/refs%2Fpr%2F7/model.safetensors
222 kB
- Xet hash:
- 62cd10881099a22536e8435d367fa1b86db9c710fca9e1f593072c1badf350dc
- Size of remote file:
- 222 kB
- SHA256:
- 0576356d46f55766568660387728afabb9dcb2446f9a56c2c09b486a3a1942c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.